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Databricks-Machine-Learning-Professional - Databricks Certified Machine Learning Professional

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Databricks-Machine-Learning-Professional Questions and Answers

Question # 1

A data scientist has developed a modelmodeland computed the RMSE of the model on the test set. They have assigned this value to the variablermse. They now want to manually store the RMSE value with the MLflow run.

They write the following incomplete code block:

Which of the following lines of code can be used to fill in the blank so the code block can successfully complete the task?

A.

log_artifact

B.

log_model

C.

log_metric

D.

log_param

E.

There is no way to store values like this.

Question # 2

Which of the following lists all of the model stages are available in the MLflow Model Registry?

A.

Development. Staging. Production

B.

None. Staging. Production

C.

Staging. Production. Archived

D.

None. Staging. Production. Archived

E.

Development. Staging. Production. Archived

Question # 3

A machine learning engineer is migrating a machine learning pipeline to use Databricks Machine Learning. They have programmatically identified the best run from an MLflow Experiment and stored its URI in themodel_urivariable and its Run ID in therun_idvariable. They have also determined that the model was logged with the name"model". Now, the machine learning engineer wants to register that model in the MLflow Model Registry with the name"best_model".

Which of the following lines of code can they use to register the model to the MLflow Model Registry?

A.

mlflow.register_model(model_uri, "best_model")

B.

mlflow.register_model(run_id, "best_model")

C.

mlflow.register_model(f"runs:/{run_id}/best_model", "model")

D.

mlflow.register_model(model_uri, "model")

E.

mlflow.register_model(f"runs:/{run_id}/model")

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